Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

The user’s intent to seek online information has been an active area of research in user profiling.User profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and motivations.Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation.The user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social platforms.However,a combination of multiple behaviors in profiling users has yet to be considered.This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition.This research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning.The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation.User feedback is based on online behavior and practices collected by using a survey method.The participants include both males and females from different occupation sectors and different ages.The data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics.Different techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136.Feature average is computed to identify user intent type characteristics.The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on their preferences in online content,platforms,criteria,and frequency.The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

A mixed stochastic user equilibrium model considering influence of advanced traveller information systems in degradable transport network

Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network capacity degradations. In this paper, a mixed stochastic user equilibrium model was proposed to describe the interactive route choice behaviors between ATIS equipped and unequipped drivers on a degradable transport network. In the proposed model the information accessibility of equipped drivers was reflected by lower degree of uncertainty in their stochastic equilibrium flow distributions, and their behavioral adaptability was captured by multiple equilibrium behaviors over the stochastic network state set. The mixed equilibrium model was formulated as a fixed point problem defined in the mixed route flows, and its solution was achieved by executing an iterative algorithm. Numerical experiments were provided to verify the properties of the mixed network equilibrium model and the efficiency of the iterative algorithm.

Introduction to information seeking behavior—A review of literature and field practice directions

The article tries to discover the major authors in the field of information seeking behavior via social network analysis. It is to be accomplished through a literature review and also by focusing on a graphic map showing the seven most productive coauthors in this field. Based on these seven authors' work, five probable research directions about information seeking behavior are discerned and presented.

Subtle role of latency for information diffusion in online social networks

Information diffusion in online social networks is induced by the event of forwarding information for users, and latency exists widely in user spreading behaviors. Little work has been done to reveal the effect of latency on the diffusion process. In this paper, we propose a propagation model in which nodes may suspend their spreading actions for a waiting period of stochastic length. These latent nodes may recover their activity again. Meanwhile, the mechanism of forwarding information is also introduced into the diffusion model. Mean-field analysis and numerical simulations indicate that our model has three nontrivial results. First, the spreading threshold does not correlate with latency in neither homogeneous nor heterogeneous networks, but depends on the spreading and refractory parameter. Furthermore, latency affects the diffusion process and changes the infection scale. A large or small latency parameter leads to a larger final diffusion extent, but the intrinsic dynamics is different. Large latency implies forwarding information rapidly, while small latency prevents nodes from dropping out of interactions. In addition, the betweenness is a better descriptor to identify influential nodes in the model with latency, compared with the coreness and degree. These results are helpful in understanding some collective phenomena of the diffusion process and taking measures to restrain a rumor in social networks.

Impact of Online Community Structure on Information Propagation:Empirical Analysis and Modeling

Online social networking sites ( OSNS) ,as a popular social media platform,have been developed massively for business and research purposes. In this paper,it investigated the impact of community structure in online social network on information propagation. A SI (Susceptible-Infected) model based on community structure was proposed. In the SI model,the heterogeneity of user's active time was taken into account. From the results,it was found that the number of links among communities determines the fraction of infected nodes. With the increase of the number of groups G,however,the fraction of infected nodes remains approximately constant. The simulation results will be of great significance: the information will last relatively short for group networks which have either a small or a large number of groups. The results can be useful for optimizing or controlling information,such as propagating rumors in online social networks.

Obsolescence determination of network information based on “double-proportion” method

Purpose:It is necessary to determine whether the value of network information exists or not from users’perspective.This study is to develop a new approach,called'double-proportion'method(DPM),to determine the point of information obsolescence.Design/methodology/approach:Firstly,DPM is produced and introduced with statistics in great detail.Secondly,two data sets from Qiangguo Forum and NetEase News are used to validate DPM.Finally,the distributions of lifetime of the two sets are compared and reasons for the differences are revealed.Findings:In Qiangguo Forum,posts with no comments within 17 h can be identified as obsolescence,which indicates that approximately 83%of posts information has died within24 h,92%died within 36 h and almost all died within 54 h;While in NetEase News,news without comments within 28 h can be identified as obsolescence.Research limitations:This study adopted comments as the only behavior indicator,for further study other indicators like page views,republished times need to be used,to exploit better solutions for information obsolescence.Practical implications:According to obsolescence determination,network managers can optimize webpages more rationally,delete aged information in time,preserve valuable information,help search engines ignore aged webpages,optimize reptiles’mechanisms,and help advertisers choose webpages rationally to advertise online.Originality/value:Firstly,DPM is designed from users’perspective to describe the lifetime of information;Secondly,DPM is easy to use.DPM merges and improves former methods by taking the long-tail theory and Pareto principle into consideration.

Behavioral Intention to Continue Using a Library Mobile App

To meet the needs of today’s library users,institutions are developing library mobile apps(LMAs),as their libraries are increasingly intelligent and rely on deep learning.This paper explores the influencing factors and differences in the perception of LMAs at different time points after a user has downloaded an LMA.A research model was constructed based on the technology acceptance model.A questionnaire was designed and distributed twice to LMA users with an interval of three months to collect dynamic data.The analysis was based on structural equation modeling.The empirical results show that the perceived ease of use,the perceived usefulness,the social influence,and the facilitating conditions affected the users’behavioral intention,but their impacts were different at different times.As the usage time increases,the technology acceptance model is still universal for understanding the user perception of LMA.In addition,two extended variables(social impact and convenience)also affect the user’s behavior intention.User behavior is dynamic and changed over time.This study is important both theoretically and practically,as the results could be used to improve the service quality of LMAs and reduce the loss rate of users.Its findings may help the designers and developers of LMAs to optimize them from the perspective of a user and improve the service experience by providing a deeper understanding of the adoption behavior of information systems by LMA users.

用户跨社交媒体信息行为研究——概念界定、主题归类与未来展望

[目的/意义]通过对过去10余年国内外相关文献的梳理,把握用户跨社交媒体信息行为研究的特征和不足,并提出值得关注的研究方向,为该领域的发展和理论突破提供助力。[方法/过程]在文献检索的基础上,根据研究目的和质量评估筛选文献,再通过内容分析的方式从概念界定、理论基础和主题归类等方面进行总结整理。[结果/结论]用户跨社交媒体信息行为研究具有用户类型多样性、社交媒体异质性、信息线索一致性、行为模式协同性等概念内涵,理论基础聚焦在研究背景、信息需求和信息行为等维度,研究主题可归类为不同用户在不同社交媒体中的信息行为和同一用户在不同社交媒体中的信息行为。未来研究应当进一步扩展概念特征的外延,构建专有的理论模型和深挖其实践价值。

近十年国内信息用户与行为研究主题变迁及趋势研究——基于2011—2020年国家社科立项项目

[目的/意义]信息用户与行为一直是图情领域的研究热点,通过国家社科项目立项数据,分析近十年来我国信息用户与行为研究主题的特点与发展趋势。[方法/过程]收集2011–2020年十年间信息用户与行为研究相关的国社科立项指南、立项项目及相关发文成果信息。分析项目及其成果发文的外部计量特征,并借助可视化分析软件CiteSpace进行内容分析,揭示国内信息用户与行为研究的主题特点和变迁。[结果/结论]图书馆读者相关的服务研究及网络相关信息行为研究是十年间的长线研究主题,十二五时期与十三五时期研究内容、依托理论、研究方法具有一定差异。未来研究将围绕服务国家重大战略、服务人工智能等新科技发展需求、推进“以用户为中心”研究理念、深化相关影响因素研究等问题开展。

直播购物情境下用户贡献行为影响因素研究

直播购物如何促进用户作出贡献行为是直播购物发展的关键。基于SOR理论,构建直播购物情境下用户贡献行为影响模型。研究发现,信息质量、品牌形象、主播专业性与直播互动性对感知愉悦与感知信任有正向影响;感知愉悦与感知信任对消费者冲动性消费与营销信息分享有正向影响;刺激环节的变量均通过感知愉悦与感知信任对消费者冲动性消费与营销信息分享的贡献行为产生正向影响;信息质量、品牌形象不能直接对冲动性消费产生影响,且品牌形象直接对消费者的营销信息分享产生正向影响;主播专业性、直播互动性不能直接对营销信息分享产生影响,对消费者的冲动性消费产生负向影响。

信息指引:内涵、应用及概念辨析

阐述了信息行为研究背景下信息指引概念产生的必要性;通过分析信息指引在缓解实际研究与运用中术语运用混淆、不规范等情况的作用,适时提出了既能够突出信息行为主体,又兼顾整个信息行为系统的信息指引概念,并指出信息指引具有资源收集的针对性与全面性、指引过程的自然性、成本的可控性、受众行为反应的指向性等特点;最后对信息指引的应用领域与方法进行了拓展。

在线问答社区学生用户信息资源隐藏行为的影响因素研究

[目的/意义]研究信息资源付费如何影响学生用户信息资源隐藏行为,为减少在线问答社区信息资源隐藏行为的相关理论与实践提供参考。[方法/过程]针对在线问答社区学生用户,采用问卷调查法收集了216份有效问卷,构建信息资源付费影响在线问答社区学生用户信息资源隐藏行为的链式中介模型,基于资源保存理论探究其内部影响机理,并探讨感知成本和感知损失在该模型中的双中介作用。[结果/结论]信息资源付费直接正向影响在线问答社区学生用户信息资源隐藏行为;信息资源付费分别以感知成本和感知损失为中介变量来正向影响在线问答社区学生用户信息资源隐藏行为。

基于CPSS视角的需求响应能力评估综述

对信息—物理—社会系统与需求响应评估的结合进行综述。首先对信息物理系统的历史和框架进行分析。接着总结了现有的需求响应研究现状,包括需求响应潜力评估的意义、分类和评估方法。对于需求响应能力评估的数据来源进行总结,包括问卷调查和运行数据采集。在信息—物理—社会系统与需求响应结合方面,分别分析需求响应的物理域、信息域和社会域基础,介绍相应的建模方法和研究内容。最后,对未来的研究展望进行讨论,包括市场考核机制、快速仿真与建模技术以及综合能源下的需求响应管理。

AIGC用户中辍行为影响因素模型构建与实证研究

[目的/意义]为了厘清AIGC情境下用户心理韧性的驱动因素以及AIGC用户中辍行为的特征规律,有效缓解AIGC用户在初始采纳阶段后流失和中辍等消极行为造成的潜在风险,刺激AIGC用户持续使用,促进AIGC产业由技术驱动向用户驱动的高质量转变。[方法/过程]研究以韧性理论和S-O-R理论为基础,构建了AIGC用户中辍行为影响因素研究模型,通过发放问卷的形式,搜集了328份原始数据对所构建的模型进行实证和检验。[结果/结论]研究结果显示,心理韧性是有效缓解AIGC用户中辍行为的重要因素,技术韧性和信息质量是提升用户心理韧性的重要驱动因素,以此为基础,提出了提升用户心理韧性、防止用户中辍,促进用户持续使用的对策与建议。

网红图书馆类型对用户参与行为的影响研究

文章基于信息处理流畅性理论,通过实验探究网红图书馆类型与用户生成品牌内容诉求对用户参与行为的影响机制。研究发现,当温暖型网红图书馆与用户生成品牌内容情感性诉求相匹配时,用户线上参与行为更加积极;当能力型网红图书馆与用户生成品牌内容功能性诉求相匹配时,用户线下参与行为更加积极;自我表达需求在以上影响中发挥中介效应,自我建构在以上影响中发挥调节效应。基于此,提出图书馆利用网红现象提升影响力的策略,即挖掘自身优势资源,明确特色定位;建立在线数字平台,树立品牌形象;把握数字营销机遇,增强用户黏性;重视用户生成内容,鼓励用户参与。

互动仪式链理论及其在信息系统研究领域的应用与展望

[目的/意义]本文旨在厘清互动仪式链理论的发展历程及其在信息系统研究领域中的应用现状,填补国内信息系统研究领域在使用互动仪式链理论过程中的空缺。[方法/过程]本文对国内外以互动仪式链为理论基础的文献进行整理归纳,总结得出互动仪式链在信息系统研究中取得的成果、局限和未来值得关注的研究方向。[结果/结论]目前互动仪式链理论在信息系统领域的研究主要集中于社会化媒体用户行为研究、信息技术服务研究以及知识分享行为研究3个方面,鉴于现有研究的局限性,未来研究可从研究对象差异化、研究方法多样化、用户群体细分化、研究方向创新4个角度切入,不断丰富互动仪式链理论在信息系统领域的研究成果。

用户信息选择性接触行为关键影响因素识别研究

[目的/意义]用户信息选择性接触行为屡见不鲜,严重影响个体全面发展、群体有效沟通以及社会民主进程。识别与分析用户信息选择性接触行为的关键影响因素,对规避选择性接触、提高用户信息多样性具有重要实践意义。[方法/过程]基于文献调研构建了符合信息生态理论的用户信息选择性接触影响因素体系,通过专家调查问卷获得用户信息选择性接触行为各影响因素间的直接影响矩阵,运用Fuzzy-DEMATEL方法对选择性接触影响因素的因果关系和相对重要程度进行分析,识别出选择性接触的关键影响因素。[结果/结论]用户信息选择性接触行为的关键影响因素包括:信息来源、客观性错觉、感知威胁、可定制技术、意见领袖。根据实证研究结论,提出了相应的信息用户规避选择性接触的对策建议。[局限]研究采用小样本调查,研究结论的稳健性有待进一步检验。

跨文化传播视角下多语言用户信息分享行为过程模型构建研究

[目的/意义]全球化使得个体与不同文化的接触日益频繁,掌握多种语言的个体能够借助多元文化融合的机遇,利用数字平台为中华优秀文化的交流和传播提供新的路径和方向。[方法/过程]社交媒体时代多语言用户成为中国文化跨文化传播的核心推动者。故此,文章以多语言用户,即掌握两种及以上语言的用户作为研究对象探究其如何借助信息分享行为传播中华文化相关内容。通过众包方式收集来自全球355个多语言用户跨文化信息分享数据,采用主题分析法构建了多语言用户信息分享行为过程模型,旨在揭示具体的形成机制和行为规律。[结果/结论]构建了包含动机驱动阶段、信息交互阶段和体验感知阶段的多语言用户信息分享行为过程模型。立足中国文化语境,有助于凸显跨文化研究情境,对于促进新时代跨文化传播具有重要意义。

网络结构视角下用户异质性对知识协作效果的影响研究

[目的/意义]探讨如何优化在线知识协作平台网络结构和平衡团队内部的知识与经验,为促进跨领域信息流通,避免信息孤岛的形成,通过集体知识协作,推动知识的创造、传播和应用。[方法/过程]本研究利用编程问答网站Stack Overflow的数据,筛选出含“Python”标签的问题帖和对应的回答帖,结合负二项回归模型探讨了网络结构特征和用户异质性对知识协作质量和效率的影响。[结果/结论]网络结构特征方面,节点中心性显著提升了协作的质量和效率,较高的聚集系数和较大的连边跨度限制了信息流通,不利于知识协作效率。用户异质性方面,知识背景和注册时长的高异质性通常阻碍协作,经验异质性中注册时长异质性对协作效果均产生负面影响,回答被采纳率异质性仅对协作质量产生了负面影响,而活动强度异质性则产生了积极影响。